Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Jul 19, 2022

Abstract

BACKGROUND: Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized.

Authors

  • Andrew M Schmidt
    Department of Radiology, Stanford University, Palo Alto, California, USA.
  • Arjun D Desai
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Lauren E Watkins
    Department of Radiology, Stanford University, Palo Alto, California, USA.
  • Hollis A Crowder
    Mechanical Engineering, Stanford University, Palo Alto, California, USA.
  • Marianne S Black
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Valentina Mazzoli
    Department of Radiology, Stanford University, Palo Alto, California, USA.
  • Elka B Rubin
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Quin Lu
    Philips Healthcare North America, Gainesville, USA.
  • James W MacKay
    Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Norwich Medical School, University of East Anglia, Norwich, United Kingdom.
  • Robert D Boutin
    Department of Radiology, University of California, Davis, School of Medicine, Sacramento, California.
  • Feliks Kogan
    Department of Radiology, Stanford University, Stanford, California.
  • Garry E Gold
    Department of Radiology, Stanford University, Stanford, California.
  • Brian A Hargreaves
    Department of Radiology, Stanford University, Stanford, California.
  • Akshay S Chaudhari
    Department of Radiology, Stanford University, Stanford, California.

Keywords

External Resources

Popular Topics
Recent Journals